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Aiming at leaching solution of indium residue produced by zinc hydrometallurgy, rotating disk extraction tower was used to separate indium and iron from the solution. The extraction effects of indium and iron ions in the solution, the extraction sequence of metal ions, the separation effect and emulsification of light and heavy phases was studied. The influences of the rotating disk rotating speed, feed flow rate, concentration of P204, solution acidity, reaction temperature and the phase ratio on separation effects were examined. The results show that under the optimal conditions of reaction temperature of 25 °C, heavy phase volume flow rate of 7. 8 L/h, P204 concentration of 20%, rotating disk rotating speed of 360 r/min, initial acidity of 60 g/L, Vo/VA phase ratio of 1/1, the extraction rate of indium can reach 75%, the extraction rate of impurity element iron is less than 5%. The light and heavy phases are basically completely separated, and there is no emulsification. The result of extraction isotherms shows that complete extraction of indium can be achieved by three-stage countercurrent extraction under the condition of Vo/VA phase ratio of 1/1. The loaded organic phase is pre-washed with dilute sulfuric acid, and then stripping with 5 mol/L hydrochloric acid under the condition of Vo/VA = 1/1. And the extraction rate of indium is more than 98%. After the replacement treatment, the stripping solution can be directly returned to zinc hydrometallurgy, the organic phase can be recycled after scrubbing. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have transformed natural language processing, enabling advanced human-machine communication. Similarly, in computational biology, protein sequences are interpreted as natural language, facilitating the creation of protein large language models (PLLMs). However, applying PLLMs requires specialized preprocessing and script development, increasing the complexity of their use. Researchers have integrated LLMs with PLLMs to develop automated protein analysis tools to address these challenges, simplifying analytical workflows. Existing technologies often require substantial human intervention for specific protein-related tasks, maintaining high barriers to implementing automated protein analysis systems. Here, we propose ProtChat, an AI multiagent system for protein analysis that integrates the inference capabilities of PLLMs with the task-planning abilities of LLMs. ProtChat integrates GPT-4 with multiple PLLMs, like ESM and MASSA, to automate tasks such as protein property prediction and protein-drug interactions without human intervention. This AI agent enables users to input instructions directly, significantly improving efficiency and usability, making it suitable for researchers without a computational background. Experiments demonstrate that ProtChat can automate complex protein tasks accurately, avoiding manual intervention and delivering results rapidly. This advancement opens new research avenues in computational biology and drug discovery. Future applications may extend ProtChat's capabilities to broader biological data analysis. Our code and data are publicly available at github.com/SIAT-code/ProtChat.